{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mts/SMAP_MSL/data\n"
     ]
    }
   ],
   "source": [
    "%cd '../../../../mts/SMAP_MSL/data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/mts/SMAP_MSL/data'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_folder = '../../../../mts/SMAP_MSL/data/train/'\n",
    "test_folder = '../../../../mts/SMAP_MSL/data/test/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "labeled_anomalies = pd.read_csv('../../../../mts/SMAP_MSL/data/labeled_anomalies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chan_id</th>\n",
       "      <th>spacecraft</th>\n",
       "      <th>anomaly_sequences</th>\n",
       "      <th>class</th>\n",
       "      <th>num_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-1</td>\n",
       "      <td>SMAP</td>\n",
       "      <td>[[2149, 2349], [4536, 4844], [3539, 3779]]</td>\n",
       "      <td>[contextual, contextual, contextual]</td>\n",
       "      <td>8505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S-1</td>\n",
       "      <td>SMAP</td>\n",
       "      <td>[[5300, 5747]]</td>\n",
       "      <td>[point]</td>\n",
       "      <td>7331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E-1</td>\n",
       "      <td>SMAP</td>\n",
       "      <td>[[5000, 5030], [5610, 6086]]</td>\n",
       "      <td>[contextual, contextual]</td>\n",
       "      <td>8516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E-2</td>\n",
       "      <td>SMAP</td>\n",
       "      <td>[[5598, 6995]]</td>\n",
       "      <td>[point]</td>\n",
       "      <td>8532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E-3</td>\n",
       "      <td>SMAP</td>\n",
       "      <td>[[5094, 8306]]</td>\n",
       "      <td>[point]</td>\n",
       "      <td>8307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chan_id spacecraft                           anomaly_sequences  \\\n",
       "0     P-1       SMAP  [[2149, 2349], [4536, 4844], [3539, 3779]]   \n",
       "1     S-1       SMAP                              [[5300, 5747]]   \n",
       "2     E-1       SMAP                [[5000, 5030], [5610, 6086]]   \n",
       "3     E-2       SMAP                              [[5598, 6995]]   \n",
       "4     E-3       SMAP                              [[5094, 8306]]   \n",
       "\n",
       "                                  class  num_values  \n",
       "0  [contextual, contextual, contextual]        8505  \n",
       "1                               [point]        7331  \n",
       "2              [contextual, contextual]        8516  \n",
       "3                               [point]        8532  \n",
       "4                               [point]        8307  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled_anomalies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_file = np.load(test_folder + 'A-1.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.conda/envs/py3.9test/lib/python3.9/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1)\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "def scale_data(train, test):\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1), clip=True).fit(train)\n",
    "\n",
    "    train_scaled = scaler.transform(train)\n",
    "    test_scaled = scaler.transform(test)\n",
    "\n",
    "    return train_scaled, test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chan_id</th>\n",
       "      <th>spacecraft</th>\n",
       "      <th>anomaly_sequences</th>\n",
       "      <th>class</th>\n",
       "      <th>num_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P-1</td>\n",
       "      <td>SMAP</td>\n",
       "      <td>[[2149, 2349], [4536, 4844], [3539, 3779]]</td>\n",
       "      <td>[contextual, contextual, contextual]</td>\n",
       "      <td>8505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S-1</td>\n",
       "      <td>SMAP</td>\n",
       "      <td>[[5300, 5747]]</td>\n",
       "      <td>[point]</td>\n",
       "      <td>7331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E-1</td>\n",
       "      <td>SMAP</td>\n",
       "      <td>[[5000, 5030], [5610, 6086]]</td>\n",
       "      <td>[contextual, contextual]</td>\n",
       "      <td>8516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E-2</td>\n",
       "      <td>SMAP</td>\n",
       "      <td>[[5598, 6995]]</td>\n",
       "      <td>[point]</td>\n",
       "      <td>8532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E-3</td>\n",
       "      <td>SMAP</td>\n",
       "      <td>[[5094, 8306]]</td>\n",
       "      <td>[point]</td>\n",
       "      <td>8307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chan_id spacecraft                           anomaly_sequences  \\\n",
       "0     P-1       SMAP  [[2149, 2349], [4536, 4844], [3539, 3779]]   \n",
       "1     S-1       SMAP                              [[5300, 5747]]   \n",
       "2     E-1       SMAP                [[5000, 5030], [5610, 6086]]   \n",
       "3     E-2       SMAP                              [[5598, 6995]]   \n",
       "4     E-3       SMAP                              [[5094, 8306]]   \n",
       "\n",
       "                                  class  num_values  \n",
       "0  [contextual, contextual, contextual]        8505  \n",
       "1                               [point]        7331  \n",
       "2              [contextual, contextual]        8516  \n",
       "3                               [point]        8532  \n",
       "4                               [point]        8307  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smap = labeled_anomalies[labeled_anomalies['spacecraft'] == 'SMAP']\n",
    "smap.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chan_id</th>\n",
       "      <th>spacecraft</th>\n",
       "      <th>anomaly_sequences</th>\n",
       "      <th>class</th>\n",
       "      <th>num_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>T-1</td>\n",
       "      <td>SMAP</td>\n",
       "      <td>[[2399, 3898], [6550, 6585]]</td>\n",
       "      <td>[point, contextual]</td>\n",
       "      <td>8612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   chan_id spacecraft             anomaly_sequences                class  \\\n",
       "33     T-1       SMAP  [[2399, 3898], [6550, 6585]]  [point, contextual]   \n",
       "\n",
       "    num_values  \n",
       "33        8612  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smap[smap['chan_id'] == 'T-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chan_id</th>\n",
       "      <th>spacecraft</th>\n",
       "      <th>anomaly_sequences</th>\n",
       "      <th>class</th>\n",
       "      <th>num_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>M-6</td>\n",
       "      <td>MSL</td>\n",
       "      <td>[[1850, 2030]]</td>\n",
       "      <td>[point]</td>\n",
       "      <td>2049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>M-1</td>\n",
       "      <td>MSL</td>\n",
       "      <td>[[1110, 2250]]</td>\n",
       "      <td>[contextual]</td>\n",
       "      <td>2277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>M-2</td>\n",
       "      <td>MSL</td>\n",
       "      <td>[[1110, 2250]]</td>\n",
       "      <td>[contextual]</td>\n",
       "      <td>2277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>S-2</td>\n",
       "      <td>MSL</td>\n",
       "      <td>[[900, 910]]</td>\n",
       "      <td>[point]</td>\n",
       "      <td>1827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>P-10</td>\n",
       "      <td>MSL</td>\n",
       "      <td>[[4590, 4720]]</td>\n",
       "      <td>[point]</td>\n",
       "      <td>6100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   chan_id spacecraft anomaly_sequences         class  num_values\n",
       "55     M-6        MSL    [[1850, 2030]]       [point]        2049\n",
       "56     M-1        MSL    [[1110, 2250]]  [contextual]        2277\n",
       "57     M-2        MSL    [[1110, 2250]]  [contextual]        2277\n",
       "58     S-2        MSL      [[900, 910]]       [point]        1827\n",
       "59    P-10        MSL    [[4590, 4720]]       [point]        6100"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msl = labeled_anomalies[labeled_anomalies['spacecraft'] == 'MSL']\n",
    "msl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'msl' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/root/Diff-Anomaly/TranAD/data/preprocess_smap_msl.ipynb Cell 12\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bipintilie-2.mlp.nmbapp.net/root/Diff-Anomaly/TranAD/data/preprocess_smap_msl.ipynb#X50sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m msl[msl[\u001b[39m'\u001b[39m\u001b[39mchan_id\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mT-1\u001b[39m\u001b[39m'\u001b[39m]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'msl' is not defined"
     ]
    }
   ],
   "source": [
    "msl[msl['chan_id'] == 'T-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27\n",
      "['C-1', 'C-2', 'D-14', 'D-15', 'D-16', 'F-4', 'F-5', 'F-7', 'F-8', 'M-1', 'M-2', 'M-3', 'M-4', 'M-5', 'M-6', 'M-7', 'P-10', 'P-11', 'P-14', 'P-15', 'S-2', 'T-12', 'T-13', 'T-4', 'T-5', 'T-8', 'T-9']\n"
     ]
    }
   ],
   "source": [
    "# smap_files = smap['chan_id'].values\n",
    "msl_files = msl['chan_id'].values\n",
    "# print(len(smap_files))\n",
    "print(len(msl_files))\n",
    "# print(smap_files)\n",
    "print(sorted(msl_files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original range for P-1: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for P-1: [0.0  1.0], [0.0 1.0]\n",
      "Original range for S-1: [-0.4  1.0], [-0.4 1.0]\n",
      "Scaled range for S-1: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-1: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for E-1: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-2: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for E-2: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-3: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for E-3: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-4: [-0.9999999999999998  1.0000000000000004], [-0.9999999999999998 1.0000000000000004]\n",
      "Scaled range for E-4: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-5: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for E-5: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-6: [0.0  1.0], [0.0 1.0]\n",
      "Scaled range for E-6: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-7: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for E-7: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-8: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for E-8: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-9: [-1.0  1.0000000000000018], [-1.0 1.0000000000000018]\n",
      "Scaled range for E-9: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-10: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for E-10: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-11: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for E-11: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-12: [-1.0  1.0000000000000002], [-1.0 1.0000000000000002]\n",
      "Scaled range for E-12: [0.0  1.0], [0.0 1.0]\n",
      "Original range for E-13: [-1.0  1.0000000000000002], [-1.0 1.0000000000000002]\n",
      "Scaled range for E-13: [0.0  1.0], [0.0 1.0]\n",
      "Original range for A-1: [0.0  1.0], [0.0 1.0]\n",
      "Scaled range for A-1: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-1: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for D-1: [0.0  1.0], [0.0 1.0]\n",
      "Original range for P-2: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for P-2: [0.0  1.0], [0.0 1.0]\n",
      "Original range for P-3: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for P-3: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-2: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for D-2: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-3: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for D-3: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-4: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for D-4: [0.0  1.0], [0.0 1.0]\n",
      "Original range for A-2: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for A-2: [0.0  1.0], [0.0 1.0]\n",
      "Original range for A-3: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for A-3: [0.0  1.0], [0.0 1.0]\n",
      "Original range for A-4: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for A-4: [0.0  1.0], [0.0 1.0]\n",
      "Original range for G-1: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for G-1: [0.0  1.0], [0.0 1.0]\n",
      "Original range for G-2: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for G-2: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-5: [0.0  1.0], [0.0 1.0]\n",
      "Scaled range for D-5: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-6: [-0.9999999999999999  1.0], [-0.9999999999999999 1.0]\n",
      "Scaled range for D-6: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-7: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for D-7: [0.0  1.0], [0.0 1.0]\n",
      "Original range for F-1: [-0.34  1.0], [-0.34 1.0]\n",
      "Scaled range for F-1: [0.0  1.0], [0.0 1.0]\n",
      "Original range for P-4: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for P-4: [0.0  1.0], [0.0 1.0]\n",
      "Original range for G-3: [-0.9999999999999998  1.0], [-0.9999999999999998 1.0]\n",
      "Scaled range for G-3: [0.0  1.0], [0.0 1.0]\n",
      "Original range for T-1: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for T-1: [0.0  1.0], [0.0 1.0]\n",
      "Original range for T-2: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for T-2: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-8: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for D-8: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-9: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for D-9: [0.0  1.0], [0.0 1.0]\n",
      "Original range for F-2: [-0.54  1.0], [-0.54 1.0]\n",
      "Scaled range for F-2: [0.0  1.0], [0.0 1.0]\n",
      "Original range for G-4: [0.0  1.0], [0.0 1.0]\n",
      "Scaled range for G-4: [0.0  1.0], [0.0 1.0]\n",
      "Original range for T-3: [0.0  1.0], [0.0 1.0]\n",
      "Scaled range for T-3: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-11: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for D-11: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-12: [-1.0  0.0], [-1.0 0.0]\n",
      "Scaled range for D-12: [0.0  0.0], [0.0 1.0]\n",
      "Original range for B-1: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for B-1: [0.0  1.0], [0.0 1.0]\n",
      "Original range for G-6: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for G-6: [0.0  1.0], [0.0 1.0]\n",
      "Original range for G-7: [-0.9999999999999998  1.0], [-0.9999999999999998 1.0]\n",
      "Scaled range for G-7: [0.0  1.0], [0.0 1.0]\n",
      "Original range for P-7: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for P-7: [0.0  1.0], [0.0 1.0]\n",
      "Original range for R-1: [0.0  1.0], [0.0 1.0]\n",
      "Scaled range for R-1: [0.0  1.0], [0.0 1.0]\n",
      "Original range for A-5: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for A-5: [0.0  1.0], [0.0 1.0]\n",
      "Original range for A-6: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for A-6: [0.0  1.0], [0.0 0.9999999999999999]\n",
      "Original range for A-7: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for A-7: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-13: [-1.0  0.0], [-1.0 0.0]\n",
      "Scaled range for D-13: [0.0  0.0], [0.0 1.0]\n",
      "Original range for P-2: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for P-2: [0.0  1.0], [0.0 1.0]\n",
      "Original range for A-8: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for A-8: [0.0  1.0], [0.0 1.0]\n",
      "Original range for A-9: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for A-9: [0.0  1.0], [0.0 1.0]\n",
      "Original range for F-3: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for F-3: [0.0  1.0], [0.0 1.0]\n"
     ]
    }
   ],
   "source": [
    "for file in smap_files:\n",
    "    # load\n",
    "    train_original = np.load(train_folder + f'{file}.npy')\n",
    "    test_original = np.load(test_folder + f'{file}.npy')\n",
    "    print(f'Original range for {file}: [{train_original.min()}  {train_original.max()}], [{train_original.min()} {train_original.max()}]')\n",
    "    # scale\n",
    "    train_scaled, test_scaled = scale_data(train_original, test_original)\n",
    "    print(f'Scaled range for {file}: [{train_scaled.min()}  {train_scaled.max()}], [{test_scaled.min()} {test_scaled.max()}]')\n",
    "    # build labels\n",
    "    labels = np.zeros_like(test_scaled)\n",
    "    indices = smap[smap['chan_id'] == file]['anomaly_sequences'].values[0]\n",
    "    indices = indices.replace(']', '').replace('[', '').split(', ')\n",
    "    indices = [int(i) for i in indices]\n",
    "    for i in range(0, len(indices), 2):\n",
    "        labels[indices[i]:indices[i+1], :] = 1\n",
    "    # save\n",
    "    dir = '../../TranAD/processed/SMAP'\n",
    "    os.makedirs(dir, exist_ok=True)\n",
    "    np.save(f'{dir}/{file}_train.npy', train_scaled)\n",
    "    np.save(f'{dir}/{file}_test.npy', test_scaled)\n",
    "    np.save(f'{dir}/{file}_labels.npy', labels)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original range for M-6: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for M-6: [0.0  1.0], [0.0 1.0]\n",
      "Original range for M-1: [-0.9160935512741932  2.4922982712327473], [-0.9160935512741932 2.4922982712327473]\n",
      "Scaled range for M-1: [0.0  1.0], [0.0 1.0]\n",
      "Original range for M-2: [-1.210726170949998  1.0], [-1.210726170949998 1.0]\n",
      "Scaled range for M-2: [0.0  1.0], [0.0 1.0]\n",
      "Original range for S-2: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for S-2: [0.0  1.0], [0.0 1.0]\n",
      "Original range for P-10: [0.0  1.001129464915996], [0.0 1.001129464915996]\n",
      "Scaled range for P-10: [0.0  1.0], [0.0 1.0]\n",
      "Original range for T-4: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for T-4: [0.0  1.0], [0.0 1.0]\n",
      "Original range for T-5: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for T-5: [0.0  1.0], [0.0 1.0]\n",
      "Original range for F-7: [-0.9999999999999998  1.0], [-0.9999999999999998 1.0]\n",
      "Scaled range for F-7: [0.0  1.0], [0.0 1.0]\n",
      "Original range for M-3: [-1.47721668720188  1.000070758018348], [-1.47721668720188 1.000070758018348]\n",
      "Scaled range for M-3: [0.0  1.0], [0.0 1.0]\n",
      "Original range for M-4: [-1.4654640190905774  1.00000547321409], [-1.4654640190905774 1.00000547321409]\n",
      "Scaled range for M-4: [0.0  1.0], [0.0 1.0]\n",
      "Original range for M-5: [-1.2550059949886205  1.0], [-1.2550059949886205 1.0]\n",
      "Scaled range for M-5: [0.0  1.0], [0.0 1.0]\n",
      "Original range for P-15: [0.0  1.0052196607220525], [0.0 1.0052196607220525]\n",
      "Scaled range for P-15: [0.0  1.0], [0.0 1.0]\n",
      "Original range for C-1: [-1.0  2.1934477379095165], [-1.0 2.1934477379095165]\n",
      "Scaled range for C-1: [0.0  1.0], [0.0 1.0]\n",
      "Original range for C-2: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for C-2: [0.0  1.0], [0.0 1.0]\n",
      "Original range for T-12: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for T-12: [0.0  1.0], [0.0 1.0]\n",
      "Original range for T-13: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for T-13: [0.0  1.0], [0.0 1.0]\n",
      "Original range for F-4: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for F-4: [0.0  1.0], [0.0 1.0]\n",
      "Original range for F-5: [-1.1163775338154294  4.162651279553374], [-1.1163775338154294 4.162651279553374]\n",
      "Scaled range for F-5: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-14: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for D-14: [0.0  1.0], [0.0 1.0]\n",
      "Original range for T-9: [-1.0  1.0], [-1.0 1.0]\n",
      "Scaled range for T-9: [0.0  1.0], [0.0 1.0]\n",
      "Original range for P-14: [0.0  1.0], [0.0 1.0]\n",
      "Scaled range for P-14: [0.0  1.0], [0.0 1.0]\n",
      "Original range for T-8: [-1.0  1.0294117647058822], [-1.0 1.0294117647058822]\n",
      "Scaled range for T-8: [0.0  1.0], [0.0 1.0]\n",
      "Original range for P-11: [0.0  1.0], [0.0 1.0]\n",
      "Scaled range for P-11: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-15: [-1.0  1.1915779731605738], [-1.0 1.1915779731605738]\n",
      "Scaled range for D-15: [0.0  1.0], [0.0 1.0]\n",
      "Original range for D-16: [-1.0  1.008879901529805], [-1.0 1.008879901529805]\n",
      "Scaled range for D-16: [0.0  1.0], [0.0 1.0]\n",
      "Original range for M-7: [-1.0020241085789672  1.0], [-1.0020241085789672 1.0]\n",
      "Scaled range for M-7: [0.0  1.0], [0.0 1.0]\n",
      "Original range for F-8: [-1.0  1.1304347826086958], [-1.0 1.1304347826086958]\n",
      "Scaled range for F-8: [0.0  1.0], [0.0 1.0]\n"
     ]
    }
   ],
   "source": [
    "for file in msl_files:\n",
    "    # load\n",
    "    train_original = np.load(train_folder + f'{file}.npy')\n",
    "    test_original = np.load(test_folder + f'{file}.npy')\n",
    "    print(f'Original range for {file}: [{train_original.min()}  {train_original.max()}], [{train_original.min()} {train_original.max()}]')\n",
    "    # scale\n",
    "    train_scaled, test_scaled = scale_data(train_original, test_original)\n",
    "    print(f'Scaled range for {file}: [{train_scaled.min()}  {train_scaled.max()}], [{test_scaled.min()} {test_scaled.max()}]')\n",
    "    # build labels\n",
    "    labels = np.zeros_like(test_scaled)\n",
    "    indices = msl[msl['chan_id'] == file]['anomaly_sequences'].values[0]\n",
    "    indices = indices.replace(']', '').replace('[', '').split(', ')\n",
    "    indices = [int(i) for i in indices]\n",
    "    for i in range(0, len(indices), 2):\n",
    "        labels[indices[i]:indices[i+1], :] = 1\n",
    "    # save\n",
    "    dir = '../../TranAD/processed/MSL'\n",
    "    os.makedirs(dir, exist_ok=True)\n",
    "    np.save(f'{dir}/{file}_train.npy', train_scaled)\n",
    "    np.save(f'{dir}/{file}_test.npy', test_scaled)\n",
    "    np.save(f'{dir}/{file}_labels.npy', labels)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_1_test = np.load(test_folder + 'T-1.npy')\n",
    "t_1_train = np.load(train_folder + 'T-1.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2875, 25)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_1_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min_original_test = t_1_test.min()\n",
    "max_original_test = t_1_test.max()\n",
    "min_original_train = t_1_train.min()\n",
    "max_original_train = t_1_train.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.0 1.0\n",
      "-1.0 1.0\n"
     ]
    }
   ],
   "source": [
    "print(f'{min_original_train} {max_original_train}')\n",
    "print(f'{min_original_test} {max_original_test}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.023095539152304296\n",
      "0.020554483585080625\n"
     ]
    }
   ],
   "source": [
    "print(t_1_train.mean())\n",
    "print(t_1_test.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_1_train_scaled, t_1_test_scaled = scale_data(t_1_train, t_1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_train_scaled = t_1_train_scaled.min()\n",
    "max_train_scaled = t_1_train.max()\n",
    "min_test_scaled = t_1_test_scaled.min()\n",
    "max_test_scaled = t_1_test.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 1.0\n",
      "0.0 1.0\n"
     ]
    }
   ],
   "source": [
    "print(f'{min_train_scaled} {max_train_scaled}')\n",
    "print(f'{min_test_scaled} {max_test_scaled}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0315616826196304\n",
      "0.030848537658773482\n"
     ]
    }
   ],
   "source": [
    "print(t_1_train_scaled.mean())\n",
    "print(t_1_test_scaled.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  191,   112,    88,    76,    64, 69065,    84,    76,   176,\n",
       "         1943]),\n",
       " array([-1. , -0.8, -0.6, -0.4, -0.2,  0. ,  0.2,  0.4,  0.6,  0.8,  1. ]))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(t_1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAJCCAYAAAAhudhHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABh3ElEQVR4nO3de1xUdf4/8BcglxnlEo4wkA7iJS5e0MwL2homgWStru6W5iqI6eqCpZQZ5t1MTVPLDNtWwV1jrXYlXTXMG5qJpiQpOrii6Ggy2KiAMAgC5/eHX+bXJCrgnDkD5/V8POaxnXM+cz7vD8PIa8/lc+wEQRBAREREJGP2UhdAREREJDUGIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpK9FlIX0FwIgoCqqipUV1dLXYpkHBwc0KJFC9jZ2UldChERUYMwEFlAZWUlCgoKYDQapS5FckqlEj4+PnBycpK6FCIionqz46M7Hk1NTQ3OnTsHBwcHtGnTBk5OTrI8QiIIAiorK/HLL7+guroanTt3hr09z8gSEVHTwCNEj6iyshI1NTVo164dlEql1OVISqFQwNHREZcuXUJlZSVcXFykLomIiKhe+H/hLYRHQ+7iz4GIiJoi/vUiIiIi2eMpMxHpdDoYDAar9adSqaDRaKzWHxERUXPBQCQSnU6HoKAgq955plQqodVqGYqIiIgaiIFIJAaDAUajESkz5iBI4yd6f1rdJcQsXwSDwdDgQLR27VosX74cer0eISEhWLNmDfr06SNSpURERLaHgUhkQRo/9OwUIHUZ9/XFF18gISEB69atQ9++fbF69WpERkbi7Nmz8PLykro8IiIiq+BF1TK3cuVKTJw4EePHj0dwcDDWrVsHpVKJDRs2SF0aERGR1TAQyVhlZSWysrIQHh5uWmdvb4/w8HBkZmZKWBkREZF1MRDJmMFgQHV1Nby9vc3We3t7Q6/XS1QVERGR9TEQERERkewxEMmYSqWCg4MDCgsLzdYXFhZCrVZLVBUREZH1MRDJmJOTE3r16oW9e/ea1tXU1GDv3r0IDQ2VsDIiIiLr4m33ItPqLtl0PwkJCYiOjsZTTz2FPn36YPXq1SgrK8P48eMtXCEREZHtYiASiUqlglKpRMzyRVbrU6lUQqVSNeg9L7/8Mn755RfMnTsXer0ePXr0QHp6+j0XWhMRETVndoIgCFIX0ZTdvn0b+fn58Pf3h4uLi9k2OT7L7EE/DyIiIlvFI0Qi0mg0kgcUIiIiejheVE1ERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx3mIRCTHiRmJiIiaIgYikeh0OgQFBcFoNFqtT6VSCa1Wy1BERETUQAxEIjEYDDAajUiZ+TqC2rUVvT/t5SuIWfYhDAZDvQPRwYMHsXz5cmRlZaGgoABpaWkYPny4uIUSERHZIAYikQW1a4uenTtKXUadysrKEBISgtjYWIwYMULqcoiIiCTDQCRjUVFRiIqKkroMIiIiyfEuMyIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPd5lJjLt5Ss2209paSny8vJMy/n5+cjOzoanpycndyQiIllhIBKJSqWCUqlEzLIPrdanUqmESqWqd/vjx49j0KBBpuWEhAQAQHR0NFJSUixdHhERkc1iIBKJRqOBVqu16WeZhYWFQRAEESsiIiJqGhiIRKTRaHjqiYiIqAngRdVEREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQke5yHSEQ6nc6mJ2YkIiKiuxiIRKLT6RAUFAijsdxqfSqVCmi1ufUORUuWLMGWLVuQm5sLhUKB/v37Y9myZQgICBC5UiIiItvCQCQSg8EAo7Ecf581HgF+PqL3d/ZSAV59LxkGg6HegejAgQOIi4tD7969UVVVhVmzZiEiIgJnzpxBy5YtRa6YiIjIdjAQiSzAzwc9nrDN01jp6elmyykpKfDy8kJWVhYGDhwoUVVERETWx4uqyaS4uBgA4OnpKXElRERE1sVARACAmpoaTJs2DQMGDEDXrl2lLoeIiMiqeMqMAABxcXHIycnBoUOHpC6FiIjI6hiICPHx8di+fTsOHjyItm3bSl0OERGR1TEQyZggCJg6dSrS0tKQkZEBf39/qUsiIiKSBAORjMXFxSE1NRVbt26Fq6sr9Ho9AMDd3R0KhULi6oiIiKyHgUhkZy8V2Gw/SUlJAICwsDCz9cnJyYiJibFAVURERE0DA5FIVCoVlEoFXn0v2Wp9KpUKqFSqercXBEHEaoiIiJoOBiKRaDQaaLW5fJYZERFRE8BAJCKNRsOAQkRE1ARwYkYiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPc5DJCKdTseJGYmIiJoABiKR6HQ6BAUFwmgst1qfSqUCWm1uvUNRUlISkpKScPHiRQBAly5dMHfuXERFRYlYJRERke1hIBKJwWCA0ViOj+eNR+f2PqL3d+5iAeIXJMNgMNQ7ELVt2xZLly5F586dIQgCNm7ciGHDhuHEiRPo0qWLyBUTERHZDgYikXVu74PuAbZ5GuvFF180W168eDGSkpJw5MgRBiIiIpIVBiICAFRXV+Orr75CWVkZQkNDpS6HiIjIqhiIZO7UqVMIDQ3F7du30apVK6SlpSE4OFjqsoiIiKyKt93LXEBAALKzs3H06FFMmTIF0dHROHPmjNRlERERWRWPEMmck5MTOnXqBADo1asXjh07hg8//BCffvqpxJURERFZD48QkZmamhpUVFRIXQYREZFV8QiRjCUmJiIqKgoajQa3bt1CamoqMjIysGvXLqlLIyIisioGIpGdu1hgs/1cu3YN48aNQ0FBAdzd3dG9e3fs2rULzz33nAgVEhER2S4GIpGoVCoolQrEL0i2Wp9KpQIqlare7devXy9iNURERE0HA5FINBoNtNpcPsuMiIioCWAgEpFGo2FAISIiagJ4lxkRERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHuchEpFOp+PEjERERE0AA5FIdDodgoICYTSWW61PpVIBrTa3UaFo6dKlSExMxOuvv47Vq1dbvjgiIiIbxkAkEoPBAKOxHCsXjken9j6i95d3sQAJc5NhMBgaHIiOHTuGTz/9FN27dxepOiIiItvGQCSyTu190DXQdk9jlZaWYsyYMfjss8/w7rvvSl0OERGRJHhRtczFxcVh6NChCA8Pl7oUIiIiyfAIkYxt3rwZP/74I44dOyZ1KURERJJiIJKpy5cv4/XXX8fu3bvh4uIidTlERESSYiCSqaysLFy7dg1PPvmkaV11dTUOHjyIjz/+GBUVFXBwcJCwQiIiIuthIJKpwYMH49SpU2brxo8fj8DAQMycOZNhiIiIZIWBSKZcXV3RtWtXs3UtW7ZE69at71lPRETU3DEQiSzvYkGz6oeIiKg5YiASiUqlglKpQMLcZKv1qVQqoFKpGv3+jIwMyxVDRETUhDAQiUSj0UCrzeWzzIiIiJoABiIRaTQaBhQiIqImgDNVExERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkexxHiIR6XQ6TsxIRETUBDAQiUSn0yEoKBBGY7nV+lQqFdBqc+sdiubPn48FCxaYrQsICEBubq4Y5REREdksBiKRGAwGGI3lWLZoPDr6q0Xv73y+HjPnJMNgMDToKFGXLl2wZ88e03KLFvyVICIi+eFfP5F19FcjOMh2T2O1aNECarX4gY2IiMiW8aJqmTt37hx8fX3RoUMHjBkzBjqdTuqSiIiIrI6BSMb69u2LlJQUpKenIykpCfn5+fjd736HW7duSV0aERGRVfGUmYxFRUWZ/rt79+7o27cv/Pz88OWXX2LChAkSVkZERGRdPEJEJh4eHnjiiSeQl5cndSlERERWxUBEJqWlpTh//jx8fHykLoWIiMiqGIhk7M0338SBAwdw8eJFHD58GH/4wx/g4OCA0aNHS10aERGRVfEaIpGdz9fbbD9XrlzB6NGjcf36dbRp0wZPP/00jhw5gjZt2ohQIRERke1iIBKJSqWCUqnAzDnJVutTqVRApVLVu/3mzZtFrIaIiKjpYCASiUajgVaby2eZERERNQEMRCLSaDQMKERERE0AL6omIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2eM8RCLS6XScmJGIiKgJYCASiU6nQ1BQIIzGcqv1qVQqoNXmNigU/fzzz5g5cya++eYbGI1GdOrUCcnJyXjqqadErJSIiMi2MBCJxGAwwGgsx6LF4+HfQS16f/kX9JjzTjIMBkO9A9HNmzcxYMAADBo0CN988w3atGmDc+fO4bHHHhO5WiIiItvCQCQy/w5qBAXZ5mmsZcuWoV27dkhO/v8PoPX395ewIiIiImnwomoZ27ZtG5566in86U9/gpeXF3r27InPPvtM6rKIiIisjoFIxi5cuICkpCR07twZu3btwpQpU/Daa69h48aNUpdGRERkVTxlJmM1NTV46qmn8N577wEAevbsiZycHKxbtw7R0dESV0dERGQ9PEIkYz4+PggODjZbFxQUBJ1OJ1FFRERE0mAgkrEBAwbg7NmzZuv+97//wc/PT6KKiIiIpMFAJGPTp0/HkSNH8N577yEvLw+pqan429/+hri4OKlLIyIisipeQySy/At6m+2nd+/eSEtLQ2JiIhYuXAh/f3+sXr0aY8aMEaFCIiIi22UnCIIgdRFN2e3bt5Gfnw9/f3+4uLiY1jeVmaot7X4/DyIiIlvGI0Qi0Wg00Gpz+SwzIiKiJoCBSEQajYYBhYiIqAngRdVEREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQke5yHSEQ6nY4TMxIRETUBDEQiaQqP7mjfvj0uXbp0z/q//vWvWLt2raXLIyIislkMRCIxGAwwGssxe8l4+HVQi97fpQt6vJuYDIPBUO9AdOzYMVRXV5uWc3Jy8Nxzz+FPf/qTWGUSERHZJAYikfl1UCMg2DZPY7Vp08ZseenSpejYsSOeeeYZiSoiIiKSBi+qJgBAZWUlNm3ahNjYWNjZ2UldDhERkVUxEBEA4Ouvv0ZRURFiYmKkLoWIiMjqGIgIALB+/XpERUXB19dX6lKIiIisjtcQES5duoQ9e/Zgy5YtUpdCREQkCR4hIiQnJ8PLywtDhw6VuhQiIiJJMBDJXE1NDZKTkxEdHY0WLXjAkIiI5Il/AUV26YLepvvZs2cPdDodYmNjLVwRERFREyJI6JNPPhG6desmuLq6Cq6urkK/fv2EnTt3mraXl5cLf/3rXwVPT0+hZcuWwogRIwS9Xm+2j0uXLgnPP/+8oFAohDZt2ghvvvmmcOfOHbM2+/fvF3r27Ck4OTkJHTt2FJKTky02hvLycuHMmTNCeXn5PXUplQoBgNVeSqVCuHTpksXG1hj3+3kQERHZMkmPELVt2xZLly5F586dIQgCNm7ciGHDhuHEiRPo0qULpk+fjh07duCrr76Cu7s74uPjMWLECHz//fcAgOrqagwdOhRqtRqHDx9GQUEBxo0bB0dHR7z33nsAgPz8fAwdOhSTJ0/G559/jr179+LVV1+Fj48PIiMj61VnTU0Nrl69CldX13vm6KmsrERNTQ2qq6vNZn1+/PHHkZNz2urPMnv88cfN6rC26upq1NTUoLS0FJWVlZLVQUREJAgCbt26BV9fX9jbP/gqITtBEAQr1VUvnp6eWL58Of74xz+iTZs2SE1NxR//+EcAQG5uLoKCgpCZmYl+/frhm2++wQsvvICrV6/C29sbALBu3TrMnDkTv/zyC5ycnDBz5kzs2LEDOTk5pj5GjRqFoqIipKen16umK1euoF27dnVu8/Pzw7p166BSqR5x5M2HwWDA5MmT63xOGhERkbVdvnwZbdu2fWAbm7mGqLq6Gl999RXKysoQGhqKrKws3LlzB+Hh4aY2gYGB0Gg0pkCUmZmJbt26mcIQAERGRmLKlCk4ffo0evbsiczMTLN91LaZNm3afWupqKhARUWFabk2M16+fBlubm5mbSsrK1FYWIj27dvDxcXlUX4EzcLt27dx8eJFHD9+HE5OTlKXQ0REMlZSUoJ27drB1dX1oW0lD0SnTp1CaGgobt++jVatWiEtLQ3BwcHIzs6Gk5MTPDw8zNp7e3tDr797AbFerzcLQ7Xba7c9qE1JSQnKy8uhUCjuqWnJkiVYsGDBPevd3NzuCUS3b9/GL7/8AgcHBzg4ODRs8M2Qg4MD7O3t0apVKwZEIiKyCfV5JJXkt90HBAQgOzsbR48exZQpUxAdHY0zZ85IWlNiYiKKi4tNr8uXL0taDxEREYlL8iNETk5O6NSpEwCgV69eOHbsGD788EO8/PLLqKysRFFRkdlRosLCQqjVagCAWq3GDz/8YLa/wsJC07ba/61d9+s2bm5udR4dAgBnZ2c4OztbZHxERERk+yQ/QvRbNTU1qKioQK9eveDo6Ii9e/eatp09exY6nQ6hoaEAgNDQUJw6dQrXrl0ztdm9ezfc3NwQHBxsavPrfdS2qd0HERERkaRHiBITExEVFQWNRoNbt24hNTUVGRkZ2LVrF9zd3TFhwgQkJCTA09MTbm5umDp1KkJDQ9GvXz8AQEREBIKDgzF27Fi8//770Ov1mD17NuLi4kxHeCZPnoyPP/4Yb731FmJjY7Fv3z58+eWX2LFjh5RDJyIiIhsiaSC6du0axo0bh4KCAri7u6N79+7YtWsXnnvuOQDAqlWrYG9vj5EjR6KiogKRkZH45JNPTO93cHDA9u3bMWXKFISGhqJly5aIjo7GwoULTW38/f2xY8cOTJ8+HR9++CHatm2Lv//97/Weg4iIiIiaP5ubh8gWlZSUwN3dHcXFxXXeZZafnw9/f/977qrS6XRWn5hRo9FYrb+6POjnQUREZE0P+vv9W5JfVN1c6XQ6BAUFwmgst1qfSqUCWm1uvUNRdXU15s+fj02bNkGv18PX1xcxMTGYPXt2vW5RJCIiai4YiERiMBhgNJZj+rLxaNtRLXp/V87rsWpmMgwGQ70D0bJly5CUlISNGzeiS5cuOH78OMaPHw93d3e89tprIldMRERkOxiIRNa2oxodg6U9jXU/hw8fxrBhwzB06FAAQPv27fGvf/3rnqkMiIiImjubu+2erKd///7Yu3cv/ve//wEAfvrpJxw6dAhRUVESV0ZERGRdPEIkY2+//TZKSkoQGBgIBwcHVFdXY/HixRgzZozUpREREVkVA5GMffnll/j888+RmpqKLl26IDs7G9OmTYOvry+io6OlLo+IiMhqGIhkbMaMGXj77bcxatQoAEC3bt1w6dIlLFmyhIGIiIhkhYFIxoxGI+ztzS8jc3BwQE1NjUQVEUlDqVSivFzcKTIUCgWMRqOofRBR4zEQydiLL76IxYsXQ6PRoEuXLjhx4gRWrlyJ2NhYqUsjshqlUonbIochALhdXg6lUslQRGSjGIhEduW83mb7WbNmDebMmYO//vWvuHbtGnx9ffGXv/wFc+fOFaFCIttUe2RoWmhndPNxFaWPUwW3sDrznOhHoYio8RiIRKJSqaBUKrBqZrLV+lQqFVCpVPVu7+rqitWrV2P16tXiFUXURHTzccWQrl5Sl0FEEmEgEolGo4FWmyu7Z5kRERE1RQxEItJoNAwoRERETQBnqiYiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZ4zxEItLpdJyYkYiIqAlgIBKJTqdDYFAgyo3We3aRQqlArja3QaHo1q1bmDNnDtLS0nDt2jX07NkTH374IXr37i1ipURERLaFgUgkBoMB5cZyjF8eD58Oj4veX8GFn5E842MYDIYGBaJXX30VOTk5+Oc//wlfX19s2rQJ4eHhOHPmDB5/XPy6iYiIbAEDkch8OjwOTRd/qcuoU3l5Of7zn/9g69atGDhwIABg/vz5+O9//4ukpCS8++67EldIRERkHbyoWsaqqqpQXV0NFxcXs/UKhQKHDh2SqCoiIiLrYyCSMVdXV4SGhmLRokW4evUqqqursWnTJmRmZqKgoEDq8oiIiKyGgUjm/vnPf0IQBDz++ONwdnbGRx99hNGjR8Penr8aREQkH/yrJ3MdO3bEgQMHUFpaisuXL+OHH37AnTt30KFDB6lLIyIishoGIgIAtGzZEj4+Prh58yZ27dqFYcOGSV0SERGR1fAuM5nbtWsXBEFAQEAA8vLyMGPGDAQGBmL8+PFSl0ZERGQ1DEQiK7jws033U1xcjMTERFy5cgWenp4YOXIkFi9eDEdHRwtXSEREZLsYiESiUqmgUCqQPONjq/WpUCqgUqka9J6XXnoJL730kkgVERERNQ0MRCLRaDTI1ebyWWZERERNAAORiDQaDQMKERFRE8C7zIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2OA+RiHQ6HSdmJCIiagIYiESi0+kQGBSEcqPRan0qlErkarX1DkUHDx7E8uXLkZWVhYKCAqSlpWH48OGm7YIgYN68efjss89QVFSEAQMGICkpCZ07dxZpBERERNJgIBKJwWBAudGI8e8nwqeD+EdtCi7okPzWEhgMhnoHorKyMoSEhCA2NhYjRoy4Z/v777+Pjz76CBs3boS/vz/mzJmDyMhInDlzBi4uLpYeAhERkWQYiETm00EDTRfbPKISFRWFqKioOrcJgoDVq1dj9uzZGDZsGADgH//4B7y9vfH1119j1KhR1iyViIhIVLyomuqUn58PvV6P8PBw0zp3d3f07dsXmZmZElZGRERkeQxEVCe9Xg8A8Pb2Nlvv7e1t2kZERNRcMBARERGR7DEQUZ3UajUAoLCw0Gx9YWGhaRsREVFzwUBEdfL394darcbevXtN60pKSnD06FGEhoZKWBkREZHlSRqIlixZgt69e8PV1RVeXl4YPnw4zp49a9YmLCwMdnZ2Zq/JkyebtdHpdBg6dCiUSiW8vLwwY8YMVFVVmbXJyMjAk08+CWdnZ3Tq1AkpKSliD8/mlZaWIjs7G9nZ2QDuXkidnZ0NnU4HOzs7TJs2De+++y62bduGU6dOYdy4cfD19TWbq4iIiKg5kPS2+wMHDiAuLg69e/dGVVUVZs2ahYiICJw5cwYtW7Y0tZs4cSIWLlxoWlYqlab/rq6uxtChQ6FWq3H48GEUFBRg3LhxcHR0xHvvvQfg7h/6oUOHYvLkyfj888+xd+9evPrqq/Dx8UFkZKSoYyy4oBN1/4/Sz/HjxzFo0CDTckJCAgAgOjoaKSkpeOutt1BWVoZJkyahqKgITz/9NNLT0zkHERERNTt2giAIUhdR65dffoGXlxcOHDiAgQMHArh7hKhHjx5YvXp1ne/55ptv8MILL+Dq1aumO6LWrVuHmTNn4pdffoGTkxNmzpyJHTt2ICcnx/S+UaNGoaioCOnp6Q+tq6SkBO7u7iguLoabm5vZttu3byM/Px/+/v5mQaEpzFQthvv9PIhslZ2dHQBg/YgnMaSrlyh9pOdcw4QtPwK4O8cXEVnHg/5+/5ZNTcxYXFwMAPD09DRb//nnn2PTpk1Qq9V48cUXMWfOHNNRoszMTHTr1s3s9vDIyEhMmTIFp0+fRs+ePZGZmWk2n05tm2nTptVZR0VFBSoqKkzLJSUlDR6LRqNBrlbLZ5kRERE1ATYTiGpqajBt2jQMGDAAXbt2Na1/5ZVX4OfnB19fX5w8eRIzZ87E2bNnsWXLFgB358upa66c2m0PalNSUoLy8nIoFAqzbUuWLMGCBQseeUwajYYBhYiIqAmwmUAUFxeHnJwcHDp0yGz9pEmTTP/drVs3+Pj4YPDgwTh//jw6duwoSi2JiYmm62mAu0eI2rVrJ0pfREREJD2buO0+Pj4e27dvx/79+9G2bdsHtu3bty8AIC8vD8Dd+XLqmiundtuD2ri5ud1zdAgAnJ2d4ebmZvYiIiKi5kvSQCQIAuLj45GWloZ9+/bB39//oe+pvUXcx8cHABAaGopTp07h2rVrpja7d++Gm5sbgoODTW1+PZ9ObRvOp0NERESAxIEoLi4OmzZtQmpqKlxdXaHX66HX61FeXg4AOH/+PBYtWoSsrCxcvHgR27Ztw7hx4zBw4EB0794dABAREYHg4GCMHTsWP/30E3bt2oXZs2cjLi4Ozs7OAIDJkyfjwoULeOutt5Cbm4tPPvkEX375JaZPny7Z2ImIiMh2SBqIkpKSUFxcjLCwMPj4+JheX3zxBQDAyckJe/bsQUREBAIDA/HGG29g5MiR+O9//2vah4ODA7Zv3w4HBweEhobiz3/+M8aNG2c2b5G/vz927NiB3bt3IyQkBB988AH+/ve/iz4HERERETUNkl5U/bD5ONq1a4cDBw48dD9+fn7YuXPnA9uEhYXhxIkTDaqPiIiI5MEmLqomIiIikpLN3HbfHOl0Ok7MSERE1AQwEImkKTy64+DBg1i+fDmysrJQUFCAtLQ0swe3btmyBevWrUNWVhZu3LiBEydOoEePHuIUT0REJCEGIpEYDAaUG42IXTYPPh3bi95fwfmL2DBzAQwGQ70DUVlZGUJCQhAbG4sRI0bUuf3pp5/GSy+9hIkTJ1q6ZCIiIpvBQCQyn47toQkOkLqMOkVFRSEqKuq+28eOHQsAuHjxopUqIiIikgYvqiYiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2eNdZiIrOH/RZvspLS1FXl6eaTk/Px/Z2dnw9PSERqPBjRs3oNPpcPXqVQDA2bNnAQBqtRpqtdoidRMREdkCBiKRqFQqKJRKbJi5wGp9KpRKqFSqerc/fvw4Bg0aZFpOSEgAAERHRyMlJQXbtm3D+PHjTdtHjRoFAJg3bx7mz59vmaKJiIhsAAORSDQaDXK1Wpt+dEdYWNgDH7AbExODmJgYC1RGRERk2xiIRKTRaPhsMSIioiaAF1UTERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkexxYkYR6XQ6m56pmoiIiO5iIBKJTqdDYFAQyo1Gq/WpUCqRq9XWOxQdPHgQy5cvR1ZWFgoKCpCWlobhw4cDAO7cuYPZs2dj586duHDhAtzd3REeHo6lS5fC19dXxFEQERFZHwORSAwGA8qNRsQuexc+HfxF76/gQj42zJwNg8FQ70BUVlaGkJAQxMbGYsSIEWbbjEYjfvzxR8yZMwchISG4efMmXn/9dfz+97/H8ePHxRgCERGRZBiIRObTwR+a4CCpy6hTVFQUoqKi6tzm7u6O3bt3m637+OOP0adPH+h0Op6aIyKiZoUXVVO9FRcXw87ODh4eHlKXQkREZFEMRFQvt2/fxsyZMzF69Gi4ublJXQ4REZFFMRDRQ925cwcvvfQSBEFAUlKS1OUQERFZHK8hogeqDUOXLl3Cvn37eHSIiIiaJQYiuq/aMHTu3Dns378frVu3lrokIiIiUTAQiazgQr7N9lNaWoq8vDzTcn5+PrKzs+Hp6QkfHx/88Y9/xI8//ojt27ejuroaer0eAODp6QknJyeL1U5ERCQ1BiKRqFQqKJRKbJg522p9KpRKqFSqerc/fvw4Bg0aZFpOSEgAAERHR2P+/PnYtm0bAKBHjx5m79u/fz/CwsIeuV4iIiJbwUAkEo1Gg1yt1qYf3REWFgZBEO67/UHbiIiImhMGIhFpNBpOYEhERNQE8LZ7IiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9TswoIp1OZ9MzVRMREdFdDEQi0el0CAwKQrnRaLU+FUolcrXaeoeigwcPYvny5cjKykJBQQHS0tIwfPhw0/b58+dj8+bNuHz5MpycnNCrVy8sXrwYffv2FWkERERE0mAgEonBYEC50YgJy5ZC3aGD6P3pL1zA+plvw2Aw1DsQlZWVISQkBLGxsRgxYsQ925944gl8/PHH6NChA8rLy7Fq1SpEREQgLy8Pbdq0sfQQiIiIJMNAJDJ1hw7wCw6Wuow6RUVFISoq6r7bX3nlFbPllStXYv369Th58iQGDx4sdnlERERWw4uqqV4qKyvxt7/9De7u7ggJCZG6HCIiIoviESJ6oO3bt2PUqFEwGo3w8fHB7t27oVKppC6LiIjIoniEiB5o0KBByM7OxuHDhzFkyBC89NJLuHbtmtRlERERWZSkgWjJkiXo3bs3XF1d4eXlheHDh+Ps2bNmbW7fvo24uDi0bt0arVq1wsiRI1FYWGjWRqfTYejQoVAqlfDy8sKMGTNQVVVl1iYjIwNPPvkknJ2d0alTJ6SkpIg9vGahZcuW6NSpE/r164f169ejRYsWWL9+vdRlERERWZSkgejAgQOIi4vDkSNHsHv3bty5cwcREREoKysztZk+fTr++9//4quvvsKBAwdw9epVszuiqqurMXToUFRWVuLw4cPYuHEjUlJSMHfuXFOb/Px8DB061HS0Y9q0aXj11Vexa9cuq463OaipqUFFRYXUZRAREVmUpNcQpaenmy2npKTAy8sLWVlZGDhwIIqLi7F+/Xqkpqbi2WefBQAkJycjKCgIR44cQb9+/fDtt9/izJkz2LNnD7y9vdGjRw8sWrQIM2fOxPz58+Hk5IR169bB398fH3zwAQAgKCgIhw4dwqpVqxAZGSnqGPUXLoi6/0fpp7S0FHl5eabl/Px8ZGdnw9PTE61bt8bixYvx+9//Hj4+PjAYDFi7di1+/vln/OlPf7Jk6URERJKzqYuqi4uLAQCenp4AgKysLNy5cwfh4eGmNoGBgdBoNMjMzES/fv2QmZmJbt26wdvb29QmMjISU6ZMwenTp9GzZ09kZmaa7aO2zbRp0+qso6KiwuwoSElJSYPHolKpoFAqsX7m2w1+b2MplMoGXfB8/PhxDBo0yLSckJAAAIiOjsa6deuQm5uLjRs3wmAwoHXr1ujduze+++47dOnSxeK1ExERSclmAlFNTQ2mTZuGAQMGoGvXrgAAvV4PJycneHh4mLX19vaGXq83tfl1GKrdXrvtQW1KSkpQXl4OhUJhtm3JkiVYsGDBI41Ho9EgV6u16Ud3hIWFQRCE+27fsmWLJcoiIiKyeTYTiOLi4pCTk4NDhw5JXQoSExNNR0uAu0eI2rVr1+D9aDQaPluMiIioCbCJQBQfH4/t27fj4MGDaNu2rWm9Wq1GZWUlioqKzI4SFRYWQq1Wm9r88MMPZvurvQvt121+e2daYWEh3Nzc7jk6BADOzs5wdna2yNiIiIjI9kl6l5kgCIiPj0daWhr27dsHf39/s+29evWCo6Mj9u7da1p39uxZ6HQ6hIaGAgBCQ0Nx6tQps7lxdu/eDTc3NwT/3yMzQkNDzfZR26Z2H0RERCRvkh4hiouLQ2pqKrZu3QpXV1fTNT/u7u5QKBRwd3fHhAkTkJCQAE9PT7i5uWHq1KkIDQ1Fv379AAAREREIDg7G2LFj8f7770Ov12P27NmIi4szHeWZPHkyPv74Y7z11luIjY3Fvn378OWXX2LHjh2SjZ2IiIhsh6RHiJKSklBcXIywsDD4+PiYXl988YWpzapVq/DCCy9g5MiRGDhwINRqtdnFvg4ODti+fTscHBwQGhqKP//5zxg3bhwWLlxoauPv748dO3Zg9+7dCAkJwQcffIC///3vot9yT0RERE2DpEeIHnSHUy0XFxesXbsWa9euvW8bPz8/7Ny584H7CQsLw4kTJxpcIxERETV/fJYZERERyR4DEREREckeAxERERHJnk3MQ9Rc6XQ6m56pmoiIiO5iIBKJTqdDYFAQyo1Gq/WpUCqRq9XWOxQdPHgQy5cvR1ZWFgoKCpCWlobhw4fX2Xby5Mn49NNPsWrVqvs+A46IiKipYiASicFgQLnRiAlLV0DdoaPo/ekvnMf6t9+EwWCodyAqKytDSEgIYmNjMWLEiPu2S0tLw5EjR+Dr62upcomIiGwKA5HI1B06wi/YNp8OHxUVhaioqAe2+fnnnzF16lTs2rULQ4cOtVJlRERE1sWLqum+ampqMHbsWMyYMQNduthmqCMiIrIEBiK6r2XLlqFFixZ47bXXpC6FiIhIVDxlRnXKysrChx9+iB9//BF2dnZSl0NERCQqHiGiOn333Xe4du0aNBoNWrRogRYtWuDSpUt444030L59e6nLIyIisigeIaI6jR07FuHh4WbrIiMjMXbsWIwfP16iqoiIiMTBQCQy/YXzNttPaWkp8vLyTMv5+fnIzs6Gp6cnNBoNWrdubdbe0dERarUaAQEBj1wvERGRLWEgEolKpYJCqcT6t9+0Wp8KpRIqlare7Y8fP45BgwaZlhMSEgAA0dHRSElJsXR5RERENouBSCQajQa5Wq1NP7ojLCwMgiDUu/3FixcbURUREZHtYyASkUaj4bPFiIiImgDeZUZERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREsseJGUWk0+lseqZqIiIiuouBSCQ6nQ6BQUEoNxqt1qdCqUSuVlvvUHTw4EEsX74cWVlZKCgoQFpaGoYPH27aHhMTg40bN5q9JzIyEunp6ZYsm4iISHIMRCIxGAwoNxoxYclq+HToJHp/BRfysD5xGgwGQ70DUVlZGUJCQhAbG4sRI0bU2WbIkCFITk42LTs7O1ukXiIiIlvCQCQynw6d4BfcTeoy6hQVFYWoqKgHtnF2doZarbZSRURERNLgRdX0QBkZGfDy8kJAQACmTJmC69evS10SERGRxfEIEd3XkCFDMGLECPj7++P8+fOYNWsWoqKikJmZCQcHB6nLIyIishgGIrqvUaNGmf67W7du6N69Ozp27IiMjAwMHjxYwsqIiIgsi6fMqN46dOgAlUqFvLw8qUshIiKyKAYiqrcrV67g+vXr8PHxkboUIiIii+IpM5EVXLDO0ZTG9FNaWmp2tCc/Px/Z2dnw9PSEp6cnFixYgJEjR0KtVuP8+fN466230KlTJ0RGRlqydCIiIskxEIlEpVJBoVRifeI0q/WpUCqhUqnq3f748eMYNGiQaTkhIQEAEB0djaSkJJw8eRIbN25EUVERfH19ERERgUWLFnEuIiIianYYiESi0WiQq9Xa9KM7wsLCIAjCfbfv2rXLEmURERHZvEYFog4dOuDYsWNo3bq12fqioiI8+eSTuHDhgkWKa+o0Gg2fLUZERNQENOqi6osXL6K6uvqe9RUVFfj5558fuSgiIiIia2rQEaJt27aZ/nvXrl1wd3c3LVdXV2Pv3r1o3769xYojIiIisoYGBaLaJ6Hb2dkhOjrabJujoyPat2+PDz74wGLFEREREVlDgwJRTU0NAMDf3x/Hjh1r0B1NRERERLaqURdV5+fnW7oOIiIiIsk0+rb7vXv3Yu/evbh27ZrpyFGtDRs2PHJhRERERNbSqEC0YMECLFy4EE899RR8fHxgZ2dn6bqIiIiIrKZRgWjdunVISUnB2LFjLV1Ps6LT6Wx6YkYiIiK6q1GBqLKyEv3797d0Lc2KTqdDYFAQyo1Gq/WpUCqRq9UyFBERETVQowLRq6++itTUVMyZM8fS9TQbBoMB5UYjXl2SBB//zqL3V5B/Dn9PnAKDwVDvQHTw4EEsX74cWVlZKCgoQFpammlqhVparRYzZ87EgQMHUFVVheDgYPznP/9h6CIiomalUYHo9u3b+Nvf/oY9e/age/fucHR0NNu+cuVKixTXHPj4d4ZfcIjUZdSprKwMISEhiI2NxYgRI+7Zfv78eTz99NOYMGECFixYADc3N5w+fRouLi4SVEtERCSeRgWikydPokePHgCAnJwcs228wLrpiIqKQlRU1H23v/POO3j++efx/vvvm9Z17NjRGqURERFZVaMC0f79+y1dB9mYmpoa7NixA2+99RYiIyNx4sQJ+Pv7IzEx8Z7TakRERE1dox7uSs3ftWvXUFpaiqVLl2LIkCH49ttv8Yc//AEjRozAgQMHpC6PiIjIohp1hGjQoEEPPDW2b9++eu3nYRf1xsTEYOPGjWbviYyMRHp6umn5xo0bmDp1Kv773//C3t4eI0eOxIcffohWrVqZ2pw8eRJxcXE4duwY2rRpg6lTp+Ktt96q52jlqXayzWHDhmH69OkAgB49euDw4cNYt24dnnnmGSnLIyIiK7LGNDJSTx3TqEBUe/1QrTt37iA7Oxs5OTn3PPT1QR52US8ADBkyBMnJyaZlZ2dns+1jxoxBQUEBdu/ejTt37mD8+PGYNGkSUlNTAQAlJSWIiIhAeHg41q1bh1OnTiE2NhYeHh6YNGlSvWuVG5VKhRYtWiA4ONhsfVBQEA4dOiRRVUREZG06nQ5BQUEwijyNjFKphFbCqWMaFYhWrVpV5/r58+ejtLS03vt52EW9wN0ApFar69ym1WqRnp6OY8eO4amnngIArFmzBs8//zxWrFgBX19ffP7556isrMSGDRvg5OSELl26IDs7GytXrmQgegAnJyf07t0bZ8+eNVv/v//9D35+fhJVRURE1mYwGGA0GpEyYw6CNOL8+6/VXULM8kUNmjrG0hr9LLO6/PnPf0afPn2wYsUKi+0zIyMDXl5eeOyxx/Dss8/i3XffRevWrQEAmZmZ8PDwMIUhAAgPD4e9vT2OHj2KP/zhD8jMzMTAgQPh5ORkahMZGYlly5bh5s2beOyxx+7ps6KiAhUVFablkpKSRtdfkH+u0e8Vu5/S0lLk5eWZlvPz85GdnQ1PT09oNBrMmDEDL7/8MgYOHIhBgwYhPT0d//3vf5GRkWHByomIqCkI0vihZ6cAqcsQjUUDUWZmpkXnqBkyZAhGjBgBf39/nD9/HrNmzUJUVBQyMzPh4OAAvV4PLy8vs/e0aNECnp6e0Ov1AAC9Xg9/f3+zNt7e3qZtdQWiJUuWYMGCBY9Uu0qlgkKpxN8TpzzSfhpCoVRCpVLVu/3x48cxaNAg03JCQgIAIDo6GikpKfjDH/6AdevWYcmSJXjttdcQEBCA//znP3j66actXjsREZGUGhWIfnu9jyAIKCgowPHjxy06e/WoUaNM/92tWzd0794dHTt2REZGBgYPHmyxfn4rMTHRFA6Au0eI2rVr16B9aDQa5Gq1Nv0ss7CwMAiC8MA2sbGxiI2NfdTSiIiIbFqjApG7u7vZsr29PQICArBw4UJERERYpLC6dOjQASqVCnl5eRg8eDDUajWuXbtm1qaqqgo3btwwXXekVqtRWFho1qZ2+X7XJjk7O99z8XZjaDQaPuKCiIioCWhUIPr1XV/WdOXKFVy/fh0+Pj4AgNDQUBQVFSErKwu9evUCcPeW/5qaGvTt29fU5p133sGdO3dMjxjZvXs3AgIC6jxdRkRERPLzSBMzZmVlYdOmTdi0aRNOnDjR4PeXlpYiOzsb2dnZAP7/Rb06nQ6lpaWYMWMGjhw5gosXL2Lv3r0YNmwYOnXqhMjISAB3bwEfMmQIJk6ciB9++AHff/894uPjMWrUKPj6+gIAXnnlFTg5OWHChAk4ffo0vvjiC3z44Ydmp8SIiIhI3hp1hOjatWsYNWoUMjIy4OHhAQAoKirCoEGDsHnzZrRp06Ze+3nQRb1JSUk4efIkNm7ciKKiIvj6+iIiIgKLFi0yO531+eefIz4+HoMHDzZNzPjRRx+Ztru7u+Pbb79FXFwcevXqBZVKhblz5/KWeyIiIjJpVCCaOnUqbt26hdOnTyMoKAgAcObMGURHR+O1117Dv/71r3rt52EX9e7ateuh+/D09DRNwng/3bt3x3fffVevmoiIiEh+GhWI0tPTsWfPHlMYAoDg4GCsXbtW1IuqiYiIiMTQqGuIampqTBco/5qjo6PpGVhERERETUWjAtGzzz6L119/HVevXjWt+/nnnzF9+nRR5wciIiIiEkOjTpl9/PHH+P3vf4/27dubJiy8fPkyunbtik2bNlm0wKbMGk8H/jWpnxRMRETUVDUqELVr1w4//vgj9uzZg9zcXAB3b4EPDw+3aHFNmU6nQ2BQEMpFfjrwrymUSuRK+KRgIiKipqpBgWjfvn2Ij4/HkSNH4Obmhueeew7PPfccAKC4uBhdunTBunXr8Lvf/U6UYpsSg8GAcqMRUxanwNc/UPT+rubnIumdmAY9KfjgwYNYvnw5srKyUFBQgLS0NAwfPty03c7Ors73vf/++5gxY4YlyiYiIrIJDQpEq1evxsSJE+Hm5nbPNnd3d/zlL3/BypUrGYh+xdc/EP5BPaUuo05lZWUICQlBbGzsPc+nA4CCggKz5W+++QYTJkzAyJEjrVUiERGRVTQoEP30009YtmzZfbdHRERgxYoVj1wUWUdUVBSioqLuu/23z3rbunUrBg0ahA4dOohdGhERkVU1KBAVFhbWebu9aWctWuCXX3555KLI9hQWFmLHjh3YuHGj1KUQERFZXINuu3/88ceRk5Nz3+0nT540PXiVmpeNGzfC1dW1zlNrRERETV2DAtHzzz+POXPm4Pbt2/dsKy8vx7x58/DCCy9YrDiyHRs2bMCYMWPg4uIidSlEREQW16BTZrNnz8aWLVvwxBNPID4+HgEBAQCA3NxcrF27FtXV1XjnnXdEKZSk89133+Hs2bP44osvpC6FiIhIFA0KRN7e3jh8+DCmTJmCxMRE04NZ7ezsEBkZibVr18Lb21uUQkk669evR69evRASEiJ1KURERKJo8MSMfn5+2LlzJ27evIm8vDwIgoDOnTvjscceE6O+Ju9qfq7N9lNaWoq8vDzTcn5+PrKzs+Hp6Wmay6ikpARfffUVPvjgA4vVSkREZGsaNVM1ADz22GPo3bu3JWtpVlQqFRRKJZLeibFanwqlEiqVqt7tjx8/jkGDBpmWExISAADR0dFISUkBAGzevBmCIGD06NEWrZWIiMiWNDoQ0YNpNBrkarU2/SyzsLAw02nP+5k0aRImTZr0qKURERHZNAYiEWk0Gj5XjIiIqAlo0G33RERERM0RAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREcke5yESkU6ns+mJGYmIiOguBiKR6HQ6BAUFwWg0Wq1PpVIJrVbLUERERNRADEQiMRgMMBqNmL0gBX7tA0Xv79LFXLw7LwYGg6HegejgwYNYvnw5srKyUFBQgLS0NAwfPty0vbS0FG+//Ta+/vprXL9+Hf7+/njttdcwefJkkUZBREQkDQYikfm1D0RAYE+py6hTWVkZQkJCEBsbixEjRtyzPSEhAfv27cOmTZvQvn17fPvtt/jrX/8KX19f/P73v5egYiIiInEwEMlYVFQUoqKi7rv98OHDiI6ORlhYGIC7D3r99NNP8cMPPzAQERFRs8K7zOi++vfvj23btuHnn3+GIAjYv38//ve//yEiIkLq0oiIiCyKR4jovtasWYNJkyahbdu2aNGiBezt7fHZZ59h4MCBUpdGRERkUQxEdF9r1qzBkSNHsG3bNvj5+eHgwYOIi4uDr68vwsPDpS6PiIjIYhiIqE7l5eWYNWsW0tLSMHToUABA9+7dkZ2djRUrVjAQERFRs8JriKhOd+7cwZ07d2Bvb/4r4uDggJqaGomqIiIiEgePEIns0sVcm+2ntLQUeXl5puX8/HxkZ2fD09MTGo0GzzzzDGbMmAGFQgE/Pz8cOHAA//jHP7By5UpLlk5ERCQ5BiKRqFQqKJVKvDsvxmp9KpVKqFSqerc/fvw4Bg0aZFpOSEgAAERHRyMlJQWbN29GYmIixowZgxs3bsDPzw+LFy/mxIxERNTsMBCJRKPRQKvV2vSzzMLCwiAIwn23q9VqJCcnW6I0IiIim8ZAJCKNRsPnihERETUBvKiaiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI/zEIlIp9PZ9MSMREREdBcDkUh0Oh2CgoJgNBqt1qdSqYRWq2UoIiIiaiAGIpEYDAYYjUYsm5OCDn6Bovd34VIuZi6KgcFgqHcgOnjwIJYvX46srCwUFBQgLS0Nw4cPN20vLCzEzJkz8e2336KoqAgDBw7EmjVr0LlzZ5FGQUREJA0GIpF18AtEcEBPqcuoU1lZGUJCQhAbG4sRI0aYbRMEAcOHD4ejoyO2bt0KNzc3rFy5EuHh4Thz5gxatmwpUdVERESWx0AkY1FRUYiKiqpz27lz53DkyBHk5OSgS5cuAICkpCSo1Wr861//wquvvmrNUomIiEQl6V1mBw8exIsvvghfX1/Y2dnh66+/NtsuCALmzp0LHx8fKBQKhIeH49y5c2Ztbty4gTFjxsDNzQ0eHh6YMGECSktLzdqcPHkSv/vd7+Di4oJ27drh/fffF3toTV5FRQUAwMXFxbTO3t4ezs7OOHTokFRlERERiULSQFR7ymbt2rV1bn///ffx0UcfYd26dTh69ChatmyJyMhI3L5929RmzJgxOH36NHbv3o3t27fj4MGDmDRpkml7SUkJIiIi4Ofnh6ysLCxfvhzz58/H3/72N9HH15QFBgZCo9EgMTERN2/eRGVlJZYtW4YrV66goKBA6vKIiIgsStJTZg86ZSMIAlavXo3Zs2dj2LBhAIB//OMf8Pb2xtdff41Ro0ZBq9UiPT0dx44dw1NPPQUAWLNmDZ5//nmsWLECvr6++Pzzz1FZWYkNGzbAyckJXbp0QXZ2NlauXGkWnMico6MjtmzZggkTJsDT0xMODg4IDw9HVFQUBEGQujwiIiKLstmJGfPz86HX6xEeHm5a5+7ujr59+yIzMxMAkJmZCQ8PD1MYAoDw8HDY29vj6NGjpjYDBw6Ek5OTqU1kZCTOnj2Lmzdv1tl3RUUFSkpKzF5y1KtXL2RnZ6OoqAgFBQVIT0/H9evX0aFDB6lLIyIisiibDUR6vR4A4O3tbbbe29vbtE2v18PLy8tse4sWLeDp6WnWpq59/LqP31qyZAnc3d1Nr3bt2j36gJowd3d3tGnTBufOncPx48dNR+yIiIiaC95lVofExEQkJCSYlktKShodii5cyrVUWRbvp7S0FHl5eabl/Px8ZGdnw9PTExqNBl999RXatGkDjUaDU6dO4fXXX8fw4cMRERFhydKJiIgkZ7OBSK1WA7g7OaCPj49pfWFhIXr06GFqc+3aNbP3VVVV4caNG6b3q9VqFBYWmrWpXa5t81vOzs5wdnZ+pPpVKhWUSiVmLop5pP00hFKphEqlqnf748ePY9CgQabl2hAYHR2NlJQUFBQUICEhwfQZjBs3DnPmzLF43URERFKz2UDk7+8PtVqNvXv3mgJQSUkJjh49iilTpgAAQkNDUVRUhKysLPTq1QsAsG/fPtTU1KBv376mNu+88w7u3LkDR0dHAMDu3bsREBCAxx57TLT6NRoNtFqtTT/LLCws7IEXSL/22mt47bXXLFEaERGRTZM0ED3slM20adPw7rvvonPnzvD398ecOXPg6+trerxEUFAQhgwZgokTJ2LdunW4c+cO4uPjMWrUKPj6+gIAXnnlFSxYsAATJkzAzJkzkZOTgw8//BCrVq0SfXwajYbPFSMiImoCJA1EDztl89Zbb6GsrAyTJk1CUVERnn76aaSnp5tNFvj5558jPj4egwcPhr29PUaOHImPPvrItN3d3R3ffvst4uLi0KtXL6hUKsydO5e33BMREZGJpIHoYads7OzssHDhQixcuPC+bTw9PZGamvrAfrp3747vvvuu0XUSERFR82azt90TERERWQsDEREREckeAxERERHJHgMRERERyR4DEREREcmezU7M2BzodDqbnpiRiIiI7mIgEolOp0NQUBCMRqPV+lQqldBqtfUORUuWLMGWLVuQm5sLhUKB/v37Y9myZQgICDC1uX37Nt544w1s3rwZFRUViIyMxCeffHLPA3OJiIiaMgYikRgMBhiNRqxJTEFnTaDo/Z3T5WLqkhgYDIZ6B6IDBw4gLi4OvXv3RlVVFWbNmoWIiAicOXMGLVu2BABMnz4dO3bswFdffQV3d3fEx8djxIgR+P7778UcDhERkVUxEImssyYQ3Tr3lLqMOqWnp5stp6SkwMvLC1lZWRg4cCCKi4uxfv16pKam4tlnnwUAJCcnIygoCEeOHEG/fv2kKJuIiMjieFE1mRQXFwO4O/s3AGRlZeHOnTsIDw83tQkMDIRGo0FmZqYkNRIREYmBgYgAADU1NZg2bRoGDBiArl27AgD0ej2cnJzg4eFh1tbb2xt6vV6CKomIiMTBU2YEAIiLi0NOTg4OHTokdSlERERWxyNEhPj4eGzfvh379+9H27ZtTevVajUqKytRVFRk1r6wsBBqtdrKVRIREYmHgUjGBEFAfHw80tLSsG/fPvj7+5tt79WrFxwdHbF3717TurNnz0Kn0yE0NNTa5RIREYmGp8xkLC4uDqmpqdi6dStcXV1N1wW5u7tDoVDA3d0dEyZMQEJCAjw9PeHm5oapU6ciNDSUd5gREVGzwkAksnO6XJvtJykpCQAQFhZmtj45ORkxMTEAgFWrVsHe3h4jR440m5iRiIioOWEgEolKpYJSqcTUJTFW61OpVEKlUtW7vSAID23j4uKCtWvXYu3atY9SGhERkU1jIBKJRqOBVqvls8yIiIiaAAYiEWk0GgYUIiKiJoB3mREREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHscR4iEel0Ok7MSERE1AQwEIlEp9MhKCgIRqPRan0qlUpotdp6h6IlS5Zgy5YtyM3NhUKhQP/+/bFs2TIEBASY2vztb39DamoqfvzxR9y6dQs3b96Eh4eHSCMgIiKSBgORSAwGA4xGIzZMS0FA20DR+zt7JRexq2NgMBjqHYgOHDiAuLg49O7dG1VVVZg1axYiIiJw5swZtGzZEgBgNBoxZMgQDBkyBImJiWIOgYiISDIMRCILaBuInh17Sl1GndLT082WU1JS4OXlhaysLAwcOBAAMG3aNABARkaGlasjIiKyHl5UTSbFxcUAAE9PT4krISIisi4GIgIA1NTUYNq0aRgwYAC6du0qdTlERERWxVNmBACIi4tDTk4ODh06JHUpREREVsdARIiPj8f27dtx8OBBtG3bVupyiIiIrI6BSMYEQcDUqVORlpaGjIwM+Pv7S10SERGRJBiIZCwuLg6pqanYunUrXF1dodfrAQDu7u5QKBQAAL1eD71ej7y8PADAqVOn4OrqCo1Gw4uviYio2WAgEtnZK7k2209SUhIAICwszGx9cnIyYmJiAADr1q3DggULTNtqb8f/dRsiIqKmjoFIJCqVCkqlErGrY6zWp1KphEqlqnd7QRAe2mb+/PmYP3/+I1RFRERk+xiIRKLRaKDVavksMyIioiaAgUhEGo2GAYWIiKgJ4MSMREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHuch0hEOp2OEzMSERE1AQxEItHpdAgKCoLRaLRan0qlElqttt6haMmSJdiyZQtyc3OhUCjQv39/LFu2DAEBAQCAGzduYN68efj222+h0+nQpk0bDB8+HIsWLYK7u7uYQyEiIrIqBiKRGAwGGI1GpExPQmDbJ0TvL/fK/xCzagoMBkO9A9GBAwcQFxeH3r17o6qqCrNmzUJERATOnDmDli1b4urVq7h69SpWrFiB4OBgXLp0CZMnT8bVq1fx73//W+QRERERWY9NB6L58+ebPWkdAAICApCbe/fJ7rdv38Ybb7yBzZs3o6KiApGRkfjkk0/g7e1taq/T6TBlyhTs378frVq1QnR0NJYsWYIWLawz9MC2T6BnxxCr9NVQ6enpZsspKSnw8vJCVlYWBg4ciK5du+I///mPaXvHjh2xePFi/PnPf0ZVVZXVfoZERERis/m/aF26dMGePXtMy7/+Izx9+nTs2LEDX331Fdzd3REfH48RI0bg+++/BwBUV1dj6NChUKvVOHz4MAoKCjBu3Dg4Ojrivffes/pYbF1xcTEAwNPT84Ft3NzcGIaIiKhZsfm/ai1atIBarb5nfXFxMdavX4/U1FQ8++yzAIDk5GQEBQXhyJEj6NevH7799lucOXMGe/bsgbe3N3r06IFFixZh5syZmD9/PpycnKw9HJtVU1ODadOmYcCAAejatWudbQwGAxYtWoRJkyZZuToiIiJx2fxt9+fOnYOvry86dOiAMWPGQKfTAQCysrJw584dhIeHm9oGBgZCo9EgMzMTAJCZmYlu3bqZnUKLjIxESUkJTp8+fd8+KyoqUFJSYvZq7uLi4pCTk4PNmzfXub2kpARDhw5FcHAw5s+fb93iiIiIRGbTgahv375ISUlBeno6kpKSkJ+fj9/97ne4desW9Ho9nJyc4OHhYfYeb29v6PV6AIBerzcLQ7Xba7fdz5IlS+Du7m56tWvXzrIDszHx8fHYvn079u/fj7Zt296z/datWxgyZAhcXV2RlpYGR0dHCaokIiISj02fMouKijL9d/fu3dG3b1/4+fnhyy+/hEKhEK3fxMREJCQkmJZLSkqaZSgSBAFTp05FWloaMjIy4O/vf0+bkpISREZGwtnZGdu2bYOLi4sElRIREYnLpo8Q/ZaHhweeeOIJ5OXlQa1Wo7KyEkVFRWZtCgsLTdccqdVqFBYW3rO9dtv9ODs7w83NzezVHMXFxWHTpk1ITU2Fq6sr9Ho99Ho9ysvLAdwNQxERESgrK8P69etRUlJialNdXS1x9URERJZj00eIfqu0tBTnz5/H2LFj0atXLzg6OmLv3r0YOXIkAODs2bPQ6XQIDQ0FAISGhmLx4sW4du0avLy8AAC7d++Gm5sbgoODrVJz7pX/2Ww/SUlJAICwsDCz9cnJyYiJicGPP/6Io0ePAgA6depk1iY/Px/t27dvVK1ERES2xqYD0ZtvvokXX3wRfn5+uHr1KubNmwcHBweMHj0a7u7umDBhAhISEuDp6Qk3NzdMnToVoaGh6NevHwAgIiICwcHBGDt2LN5//33o9XrMnj0bcXFxcHZ2FrV2lUoFpVKJmFVTRO3n15RKJVQqVb3bC4LwwO1hYWEPbUNERNQc2HQgunLlCkaPHo3r16+jTZs2ePrpp3HkyBG0adMGALBq1SrY29tj5MiRZhMz1nJwcMD27dsxZcoUhIaGomXLloiOjsbChQtFr12j0UCr1fJZZkRERE2ATQei+90CXsvFxQVr167F2rVr79vGz88PO3futHRp9aLRaBhQiIhIVGI/SFyr1Yq2b1ti04GIiIiI7s+aDxIvLS0VvQ8pMRARERE1UaYHic+YgyCNnyh9fHPsCOb/4++4ffu2KPu3FQxERERETVyQxg89OwWIsu/cy5dE2a+taVLzEBERERGJgYGIiIiIZI+BiIiIiGSPgYiIiIhkjxdVi0jsuSF+ixMzEhERNQ4DkUisOTdELaVSCa1WW+9QtGTJEmzZsgW5ublQKBTo378/li1bhoCA/3+nwl/+8hfs2bMHV69eRatWrUxtAgMDxRoGERGR1TEQicQ0N8QbqxDUttPD3/CItFfyEPPBdBgMhnoHogMHDiAuLg69e/dGVVUVZs2ahYiICJw5cwYtW7YEAPTq1QtjxoyBRqPBjRs3MH/+fERERCA/Px8ODg5iDomIiMhqGIhEFtS2E3p26ip1GXVKT083W05JSYGXlxeysrIwcOBAAMCkSZNM29u3b493330XISEhuHjxIjp27GjVeomIiMTCi6rJpLi4GADg6elZ5/aysjIkJyfD398f7dq1s2ZpREREomIgIgBATU0Npk2bhgEDBqBrV/MjWp988glatWqFVq1a4ZtvvsHu3bvh5OQkUaVERESWx0BEAIC4uDjk5ORg8+bN92wbM2YMTpw4gQMHDuCJJ57ASy+91OyfaUNERPLCa4gI8fHx2L59Ow4ePIi2bdves93d3R3u7u7o3Lkz+vXrh8ceewxpaWkYPXq0BNUSERFZHgORjAmCgKlTpyItLQ0ZGRnw9/ev13sEQUBFRYUVKiQiIrIOBiIZi4uLQ2pqKrZu3QpXV1fo9XoAd48IKRQKXLhwAV988QUiIiLQpk0bXLlyBUuXLoVCocDzzz8vcfVERESWw0AkMu2VPJvtJykpCQAQFhZmtj45ORkxMTFwcXHBd999h9WrV+PmzZvw9vbGwIEDcfjwYXh5eVmibCIiIpvAQCQSlUoFpVKJmA+mW61PpVIJlUpV7/aCIDxwu6+vL3bu3PmoZREREdk8BiKRaDQaaLVaPsuMiIioCWAgEpFGo2FAISIiagI4DxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHuchEpFOp+PEjERERE0AA5FIdDodgoKCYDQardanUqmEVqutdyhasmQJtmzZgtzcXCgUCvTv3x/Lli1DQEDAPW0FQcDzzz+P9PR0pKWlYfjw4RaunoiISDoMRCIxGAwwGo1IeXMJgtr5i96f9nI+YlYkwmAw1DsQHThwAHFxcejduzeqqqowa9YsRERE4MyZM2jZsqVZ29WrV8POzk6M0omIiCTHQCSyoHb+6NkpWOoy6pSenm62nJKSAi8vL2RlZWHgwIGm9dnZ2fjggw9w/Phx+Pj4WLtMIiIi0fGiajIpLi4GAHh6eprWGY1GvPLKK1i7di3UarVUpREREYmKgYgAADU1NZg2bRoGDBiArl27mtZPnz4d/fv3x7BhwySsjoiISFw8ZUYAgLi4OOTk5ODQoUOmddu2bcO+fftw4sQJCSsjIiISH48QEeLj47F9+3bs378fbdu2Na3ft28fzp8/Dw8PD7Ro0QItWtzNzyNHjkRYWJhE1RIREVkejxDJmCAImDp1KtLS0pCRkQF/f/O74d5++228+uqrZuu6deuGVatW4cUXX7RmqURERKJiIJKxuLg4pKamYuvWrXB1dYVerwcAuLu7Q6FQQK1W13khtUajuSc8ERERNWUMRCLTXs632X6SkpIA4J7TX8nJyYiJibFAVURERE0DA5FIVCoVlEolYlYkWq1PpUIJpVKJsrKye7a1aNECzs7OZusEQWhwH415DxERka1jIBKJRqOBVqt96LPMKisrcf78eYsEDQ8PD5SWlkKr1d6zzd7eHl26dLknFBERkXjEfqZlXf/eU+MwEIlIo9E89DEaZWVlaNGiBdqrfaBwchKljvLKSlzUF6CqqoqBiIjISqz5TMvS0lLR+2juGIhshMLJCQpnF1H7uH37tqj7r70tn4iIfvVMyxlzEKTxE6WPb44dwfx//F30f9/lgH/BZOBOVTUAID9f3Au87e3t0alTJ1H7ICJqaoI0fujZKUCUfedeviTKfuWIgUgGqmruBqJ2Ki+0UipE6ePXp+WIiIiaGgYiC6mpqZG6hIdycXK0ymm5iooKnD59GnZ2dhbfv0qleuh1WURERA3FQPSInJycYG9vj6tXr6JNmzZwcnJqUBCoqKi4+7937sDOTpwnqdz5v6M2lVV3cLuyUpQ+ysrvnr++cOEC8vPzERsbK8rRIqVSCa1Wy1BEREQWxUD0iOzt7eHv74+CggJcvXq1we+vrKyEwWBAixoBTiJdlFx2+zaulxTD/k4VXES6y6zs9m3orxvgcKsMAeUCvl+1zuJ9aHWXELN8Eb777jsEBQVZfP+1KioqRL8bj0e6SCxi3+YNWOf31xrjEPu7zlvimxYGIgtwcnKCRqNBVVUVqqurG/Te06dPY/Lkyfhy9rvo6CfO4zB2HP0eb//9E3wa/wb6hzwpTh9HDmHKyqX4/PW30bPfAFH60N+4DjsAf/7zn0XZfy072EGAuBNQuri44N///jd8fHxE64PBTn6sdZu32L+/BQUF+NMf/4Ty2+Wi7L+WNb7rAG+JbyoYiCzEzs4Ojo6OcHR0bPD7Ll26BNyugEu1OF/MyluluHTpEqpLjeL1UVqGoqIiUfZdq6isFAKADydORb/uIaL0UXsLq5h9HMo5iTc//QgvvPCCKPuvxWDXMJduGnGqoFi0fdf68ccfRekDuHtEQuzbvK31+wsASfFv4MkAcY4GW+O7zlvimxZZBaK1a9di+fLl0Ov1CAkJwZo1a9CnTx+py6IG6uT7uOi3sIrdB4Nd/Vkj2NkBWLg/F9gvbh8CgF69eonXyf9p59m6Wfz+alq3afLfdWo6ZBOIvvjiCyQkJGDdunXo27cvVq9ejcjISJw9exZeXl5Sl0cyxGD3cNYIdm8tew8HrlzAzNB+GPLE46L08cXJPKw78ROGdeqCWa9PF6UPwLpHJBgkqLmRTSBauXIlJk6ciPHjxwMA1q1bhx07dmDDhg14++23Ja6OqOlq6sHO3eXu3FwaNzeEqNuI0sd3F/QAgNaKlqKNA2CQIHoUdoIMHl9eWVkJpVKJf//73xg+fLhpfXR0NIqKirB161az9hUVFabb4QGguLgYGo0Gly9fhpubm0Vry87OxjPPPIPnOwTC06WlRfddK7/oOr6/epF9sA/28YA+JoU8iV4+rUXpY+/FK/gyVyvqOIDm95mwD/n0ceN2GXZeyMWBAwfQo0cPi+23pKQE7dq1Q1FREdzd3R/cWJCBn3/+WQAgHD582Gz9jBkzhD59+tzTft68eQLunu7niy+++OKLL76a+Ovy5csPzQqyOWXWEImJiUhISDAt19TU4MaNG2jdurXFZ1+uTa9iHH2yBc19fEDzHyPH1/Q19zFyfE2fWGMUBAG3bt2Cr6/vQ9vKIhCpVCo4ODigsLDQbH1hYSHUavU97Z2dne+5zdfDw0PMEuHm5tZsf9GB5j8+oPmPkeNr+pr7GDm+pk+MMT70VNn/EedZETbGyckJvXr1wt69e03rampqsHfvXoSGhkpYGREREdkCWRwhAoCEhARER0fjqaeeQp8+fbB69WqUlZWZ7jojIiIi+ZJNIHr55Zfxyy+/YO7cudDr9ejRowfS09Ph7e0taV3Ozs6YN2+eVWbilUJzHx/Q/MfI8TV9zX2MHF/TZwtjlMVt90REREQPIotriIiIiIgehIGIiIiIZI+BiIiIiGSPgYiIiIhkj4FIZIsXL0b//v2hVCrrPbmjIAiYO3cufHx8oFAoEB4ejnPnzpm1uXHjBsaMGQM3Nzd4eHhgwoQJKC0tFWEED9fQWi5evAg7O7s6X1999ZWpXV3bN2/ebI0hmWnMzzosLOye2idPnmzWRqfTYejQoVAqlfDy8sKMGTNQVVUl5lDq1NDx3bhxA1OnTkVAQAAUCgU0Gg1ee+01FBcXm7WT8vNbu3Yt2rdvDxcXF/Tt2xc//PDDA9t/9dVXCAwMhIuLC7p164adO3eaba/Pd9KaGjK+zz77DL/73e/w2GOP4bHHHkN4ePg97WNiYu75rIYMGSL2MB6oIWNMSUm5p34XFxezNk35M6zr3xM7OzsMHTrU1MaWPsODBw/ixRdfhK+vL+zs7PD1118/9D0ZGRl48skn4ezsjE6dOiElJeWeNg39XjeYBR4VRg8wd+5cYeXKlUJCQoLg7u5er/csXbpUcHd3F77++mvhp59+En7/+98L/v7+Qnl5uanNkCFDhJCQEOHIkSPCd999J3Tq1EkYPXq0SKN4sIbWUlVVJRQUFJi9FixYILRq1Uq4deuWqR0AITk52azdr38G1tKYn/UzzzwjTJw40az24uJi0/aqqiqha9euQnh4uHDixAlh586dgkqlEhITE8Uezj0aOr5Tp04JI0aMELZt2ybk5eUJe/fuFTp37iyMHDnSrJ1Un9/mzZsFJycnYcOGDcLp06eFiRMnCh4eHkJhYWGd7b///nvBwcFBeP/994UzZ84Is2fPFhwdHYVTp06Z2tTnO2ktDR3fK6+8Iqxdu1Y4ceKEoNVqhZiYGMHd3V24cuWKqU10dLQwZMgQs8/qxo0b1hrSPRo6xuTkZMHNzc2sfr1eb9amKX+G169fNxtbTk6O4ODgICQnJ5va2NJnuHPnTuGdd94RtmzZIgAQ0tLSHtj+woULglKpFBISEoQzZ84Ia9asERwcHIT09HRTm4b+zBqDgchKkpOT6xWIampqBLVaLSxfvty0rqioSHB2dhb+9a9/CYIgCGfOnBEACMeOHTO1+eabbwQ7Ozvh559/tnjtD2KpWnr06CHExsaaravPF0lsjR3fM888I7z++uv33b5z507B3t7e7B/tpKQkwc3NTaioqLBI7fVhqc/vyy+/FJycnIQ7d+6Y1kn1+fXp00eIi4szLVdXVwu+vr7CkiVL6mz/0ksvCUOHDjVb17dvX+Evf/mLIAj1+05aU0PH91tVVVWCq6ursHHjRtO66OhoYdiwYZYutdEaOsaH/fva3D7DVatWCa6urkJpaalpna19hrXq8+/AW2+9JXTp0sVs3csvvyxERkaalh/1Z1YfPGVmY/Lz86HX6xEeHm5a5+7ujr59+yIzMxMAkJmZCQ8PDzz11FOmNuHh4bC3t8fRo0etWq8lasnKykJ2djYmTJhwz7a4uDioVCr06dMHGzZsgGDlabMeZXyff/45VCoVunbtisTERBiNRrP9duvWzWxi0MjISJSUlOD06dOWH8h9WOp3qbi4GG5ubmjRwnyuV2t/fpWVlcjKyjL7/tjb2yM8PNz0/fmtzMxMs/bA3c+itn19vpPW0pjx/ZbRaMSdO3fg6elptj4jIwNeXl4ICAjAlClTcP36dYvWXl+NHWNpaSn8/PzQrl07DBs2zOx71Nw+w/Xr12PUqFFo2bKl2Xpb+Qwb6mHfQUv8zOpDNjNVNxV6vR4A7plB29vb27RNr9fDy8vLbHuLFi3g6elpamMtlqhl/fr1CAoKQv/+/c3WL1y4EM8++yyUSiW+/fZb/PWvf0VpaSlee+01i9X/MI0d3yuvvAI/Pz/4+vri5MmTmDlzJs6ePYstW7aY9lvXZ1y7zVos8fkZDAYsWrQIkyZNMlsvxednMBhQXV1d5882Nze3zvfc77P49fetdt392lhLY8b3WzNnzoSvr6/ZH5chQ4ZgxIgR8Pf3x/nz5zFr1ixERUUhMzMTDg4OFh3DwzRmjAEBAdiwYQO6d++O4uJirFixAv3798fp06fRtm3bZvUZ/vDDD8jJycH69evN1tvSZ9hQ9/sOlpSUoLy8HDdv3nzk3/v6YCBqhLfffhvLli17YButVovAwEArVWR59R3joyovL0dqairmzJlzz7Zfr+vZsyfKysqwfPlyi/xBFXt8vw4H3bp1g4+PDwYPHozz58+jY8eOjd5vfVnr8yspKcHQoUMRHByM+fPnm20T8/Ojxlm6dCk2b96MjIwMs4uOR40aZfrvbt26oXv37ujYsSMyMjIwePBgKUptkNDQULMHdffv3x9BQUH49NNPsWjRIgkrs7z169ejW7du6NOnj9n6pv4Z2gIGokZ44403EBMT88A2HTp0aNS+1Wo1AKCwsBA+Pj6m9YWFhejRo4epzbVr18zeV1VVhRs3bpje/6jqO8ZHreXf//43jEYjxo0b99C2ffv2xaJFi1BRUfHIz7ux1vhq9e3bFwCQl5eHjh07Qq1W33OHRGFhIQBY5DO0xvhu3bqFIUOGwNXVFWlpaXB0dHxge0t+fvejUqng4OBg+lnWKiwsvO941Gr1A9vX5ztpLY0ZX60VK1Zg6dKl2LNnD7p37/7Ath06dIBKpUJeXp7V/5g+yhhrOTo6omfPnsjLywPQfD7DsrIybN68GQsXLnxoP1J+hg11v++gm5sbFAoFHBwcHvl3ol4sdjUSPVBDL6pesWKFaV1xcXGdF1UfP37c1GbXrl2SXlTd2FqeeeaZe+5Oup93331XeOyxxxpda2NY6md96NAhAYDw008/CYLw/y+q/vUdEp9++qng5uYm3L5923IDeIjGjq+4uFjo16+f8MwzzwhlZWX16stan1+fPn2E+Ph403J1dbXw+OOPP/Ci6hdeeMFsXWho6D0XVT/oO2lNDR2fIAjCsmXLBDc3NyEzM7NefVy+fFmws7MTtm7d+sj1NkZjxvhrVVVVQkBAgDB9+nRBEJrHZygId/+OODs7CwaD4aF9SP0Z1kI9L6ru2rWr2brRo0ffc1H1o/xO1KtWi+2J6nTp0iXhxIkTptvKT5w4IZw4ccLs9vKAgABhy5YtpuWlS5cKHh4ewtatW4WTJ08Kw4YNq/O2+549ewpHjx4VDh06JHTu3FnS2+4fVMuVK1eEgIAA4ejRo2bvO3funGBnZyd888039+xz27ZtwmeffSacOnVKOHfunPDJJ58ISqVSmDt3rujj+a2Gji8vL09YuHChcPz4cSE/P1/YunWr0KFDB2HgwIGm99Tedh8RESFkZ2cL6enpQps2bSS77b4h4ysuLhb69u0rdOvWTcjLyzO7zbeqqkoQBGk/v82bNwvOzs5CSkqKcObMGWHSpEmCh4eH6Y6+sWPHCm+//bap/ffffy+0aNFCWLFihaDVaoV58+bVedv9w76T1tLQ8S1dulRwcnIS/v3vf5t9VrX/Bt26dUt48803hczMTCE/P1/Ys2eP8OSTTwqdO3e2ajh/lDEuWLBA2LVrl3D+/HkhKytLGDVqlODi4iKcPn3a1KYpf4a1nn76aeHll1++Z72tfYa3bt0y/a0DIKxcuVI4ceKEcOnSJUEQBOHtt98Wxo4da2pfe9v9jBkzBK1WK6xdu7bO2+4f9DOzBAYikUVHRwsA7nnt37/f1Ab/N19LrZqaGmHOnDmCt7e34OzsLAwePFg4e/as2X6vX78ujB49WmjVqpXg5uYmjB8/3ixkWdPDasnPz79nzIIgCImJiUK7du2E6urqe/b5zTffCD169BBatWoltGzZUggJCRHWrVtXZ1uxNXR8Op1OGDhwoODp6Sk4OzsLnTp1EmbMmGE2D5EgCMLFixeFqKgoQaFQCCqVSnjjjTfMblu3loaOb//+/XX+TgMQ8vPzBUGQ/vNbs2aNoNFoBCcnJ6FPnz7CkSNHTNueeeYZITo62qz9l19+KTzxxBOCk5OT0KVLF2HHjh1m2+vznbSmhozPz8+vzs9q3rx5giAIgtFoFCIiIoQ2bdoIjo6Ogp+fnzBx4kSL/qFpjIaMcdq0aaa23t7ewvPPPy/8+OOPZvtryp+hIAhCbm6uAED49ttv79mXrX2G9/s3onZM0dHRwjPPPHPPe3r06CE4OTkJHTp0MPubWOtBPzNLsBMEK9/HTERERGRjOA8RERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJ3v8DG0x48tfUwSoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.histplot(t_1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.histplot(t_1_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(t_1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(t_1_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_scaled_2, test_scaled_1 = scale_data2(t_1_train, t_1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(test_scaled_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2875, 25)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_1_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_1_train[:, 0].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.48529729631535756 0.7426486481576787\n",
      "-1.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.0019739897816999537 0.0019739897816999537\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.00034835113794705065 0.00034835113794705065\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.0015095215977705527 0.0015095215977705527\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "-------------\n",
      "0.008941012540640966 0.008941012540640966\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.006967022758941012 0.006967022758941012\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.00023223409196470042 0.00023223409196470042\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "-------------\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "-------------\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "-------------\n",
      "0.00023223409196470042 0.00023223409196470042\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "-------------\n",
      "0.00011611704598235021 0.00011611704598235021\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.00011611704598235021 0.00011611704598235021\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "-------------\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "-------------\n",
      "0.0013934045517882026 0.0013934045517882026\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.0013934045517882026 0.0013934045517882026\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.0006967022758941013 0.0006967022758941013\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "-------------\n",
      "0.0024384579656293545 0.0024384579656293545\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.002206223873664654 0.002206223873664654\n",
      "0.0 1.0\n",
      "0.0 1.0\n",
      "-------------\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "-------------\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "0.0 0.0\n",
      "-------------\n"
     ]
    }
   ],
   "source": [
    "for feature in range(0, 25):\n",
    "    print(f'{t_1_test[:, feature].mean()} {t_1_test_scaled[:, feature].mean()}')\n",
    "    print(f'{t_1_test[:, feature].min()} {t_1_test[:, feature].max()}')\n",
    "    print(f'{t_1_test_scaled[:, feature].min()} {t_1_test_scaled[:, feature].max()}')\n",
    "    print('-------------')\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3.9test",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "27efe6010b91a164a18a011cd71b7afbe2f076e5b83b7f8099f414d97e11e710"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
